resolution independent amplitude model priors

demos/getting_started_3.py

@@ -32,7 +32,7 @@ if __name__ == '__main__':
     position_space = ift.RGSpace([128, 128])
 
     # Setting up an amplitude model
-    A = ift.AmplitudeModel(position_space, 16, 1, 10, -4., 1, 0., 1.)
+    A = ift.AmplitudeModel(position_space, 64, 3, 0.4, -4., 1, 1., 1.)
     dummy = ift.from_random('normal', A.domain)
 
     # Building the model for a correlated signal
@@ -46,9 +46,11 @@ if __name__ == '__main__':
                                         'xi': harmonic_space
                                     })))
 
-    correlated_field = ht(power_distributor(A)*ift.FieldAdapter(domain, "xi"))
+    vol = harmonic_space.scalar_dvol
+    vol = ift.ScalingOperator(vol ** (-0.5),harmonic_space)
+    correlated_field = ht(vol(power_distributor(A))*ift.FieldAdapter(domain, "xi"))
     # alternatively to the block above one can do:
-    # correlated_field = ift.CorrelatedField(position_space, A)
+    #correlated_field = ift.CorrelatedField(position_space, A)
 
     # apply some nonlinearity
     signal = ift.positive_tanh(correlated_field)

nifty5/library/amplitude_model.py

@@ -29,7 +29,7 @@ from ..sugar import makeOp, sqrt
 
 
 def _ceps_kernel(dof_space, k, a, k0):
-    return a**2/(1+(k/(k0*dof_space.bindistances[0]))**2)**2
+    return a**2/(1+(k/k0)**2)**2
 
 
 def create_cepstrum_amplitude_field(domain, cepstrum):

nifty5/library/correlated_fields.py

@@ -44,9 +44,8 @@ def CorrelatedField(s_space, amplitude_model):
     power_distributor = PowerDistributor(h_space, p_space)
     A = power_distributor(amplitude_model)
     vol = h_space.scalar_dvol
-    #vol = 1.
     vol = ScalingOperator(vol ** (-0.5),h_space)
-    return ht(vol(A*FieldAdapter(MultiDomain.make({"xi": h_space}), "xi")))
+    return ht(vol(A)*FieldAdapter(MultiDomain.make({"xi": h_space}), "xi"))
 
 
 def MfCorrelatedField(s_space_spatial, s_space_energy, amplitude_model_spatial,